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The state of charge (SOC) and the loss of active material of the electrodes of a Li ion cell under Low Earth Orbit condition (LEO) have been estimated using Kalman filtering methods, by means of the physics-based single particle (SP) model. Zero mean Gaussian noise was added to the charge-discharge curves obtained by the SP model to generate synthetic data. Afterwards, nonlinear Filtering approaches including Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) were applied to predict the true SOC and the electrodes’ degradation, by minimizing the measurement residuals between the model prediction and the synthetic data. The results indicated that UKF is a far superior candidate than EKF for the SOC estimation for a Li-ion cell during the cycling. Moreover, the proposed method is able to predict the loss of active material for each electrode during the cell life.

State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches